Literature DB >> 24445778

Robust global microRNA expression profiling using next-generation sequencing technologies.

Shirley Tam1, Richard de Borja2, Ming-Sound Tsao3, John D McPherson4.   

Abstract

miRNAs are a class of regulatory molecules involved in a wide range of cellular functions, including growth, development and apoptosis. Given their widespread roles in biological processes, understanding their patterns of expression in normal and diseased states will provide insights into the consequences of aberrant expression. As such, global miRNA expression profiling of human malignancies is gaining popularity in both basic and clinically driven research. However, to date, the majority of such analyses have used microarrays and quantitative real-time PCR. With the introduction of digital count technologies, such as next-generation sequencing (NGS) and the NanoString nCounter System, we have at our disposal many more options. To make effective use of these different platforms, the strengths and pitfalls of several miRNA profiling technologies were assessed, including a microarray platform, NGS technologies and the NanoString nCounter System. Overall, NGS had the greatest detection sensitivity, largest dynamic range of detection and highest accuracy in differential expression analysis when compared with gold-standard quantitative real-time PCR. Its technical reproducibility was high, with intrasample correlations of at least 0.95 in all cases. Furthermore, miRNA analysis of formalin-fixed, paraffin-embedded (FFPE) tissue was also evaluated. Expression profiles between paired frozen and FFPE samples were similar, with Spearman's ρ>0.93. These results show the superior sensitivity, accuracy and robustness of NGS for the comprehensive profiling of miRNAs in both frozen and FFPE tissues.

Entities:  

Mesh:

Substances:

Year:  2014        PMID: 24445778     DOI: 10.1038/labinvest.2013.157

Source DB:  PubMed          Journal:  Lab Invest        ISSN: 0023-6837            Impact factor:   5.662


  44 in total

1.  MicroRNAs and small interfering RNAs can inhibit mRNA expression by similar mechanisms.

Authors:  Yan Zeng; Rui Yi; Bryan R Cullen
Journal:  Proc Natl Acad Sci U S A       Date:  2003-08-05       Impact factor: 11.205

2.  Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets.

Authors:  Benjamin P Lewis; Christopher B Burge; David P Bartel
Journal:  Cell       Date:  2005-01-14       Impact factor: 41.582

3.  An optimized isolation and labeling platform for accurate microRNA expression profiling.

Authors:  Jaclyn Shingara; Kerri Keiger; Jeffrey Shelton; Walairat Laosinchai-Wolf; Patricia Powers; Richard Conrad; David Brown; Emmanuel Labourier
Journal:  RNA       Date:  2005-07-25       Impact factor: 4.942

Review 4.  miRBase: the microRNA sequence database.

Authors:  Sam Griffiths-Jones
Journal:  Methods Mol Biol       Date:  2006

5.  Direct multiplexed measurement of gene expression with color-coded probe pairs.

Authors:  Gary K Geiss; Roger E Bumgarner; Brian Birditt; Timothy Dahl; Naeem Dowidar; Dwayne L Dunaway; H Perry Fell; Sean Ferree; Renee D George; Tammy Grogan; Jeffrey J James; Malini Maysuria; Jeffrey D Mitton; Paola Oliveri; Jennifer L Osborn; Tao Peng; Amber L Ratcliffe; Philippa J Webster; Eric H Davidson; Leroy Hood; Krassen Dimitrov
Journal:  Nat Biotechnol       Date:  2008-02-17       Impact factor: 54.908

6.  Analysis of tumor-host interactions by gene expression profiling of lung adenocarcinoma xenografts identifies genes involved in tumor formation.

Authors:  Chad J Creighton; Jennifer L Bromberg-White; David E Misek; David J Monsma; Frank Brichory; Rork Kuick; Thomas J Giordano; Weimin Gao; Gilbert S Omenn; Craig P Webb; Samir M Hanash
Journal:  Mol Cancer Res       Date:  2005-03       Impact factor: 5.852

7.  Unique microRNA molecular profiles in lung cancer diagnosis and prognosis.

Authors:  Nozomu Yanaihara; Natasha Caplen; Elise Bowman; Masahiro Seike; Kensuke Kumamoto; Ming Yi; Robert M Stephens; Aikou Okamoto; Jun Yokota; Tadao Tanaka; George Adrian Calin; Chang-Gong Liu; Carlo M Croce; Curtis C Harris
Journal:  Cancer Cell       Date:  2006-03       Impact factor: 31.743

8.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

Authors:  Mark D Robinson; Davis J McCarthy; Gordon K Smyth
Journal:  Bioinformatics       Date:  2009-11-11       Impact factor: 6.937

9.  Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes.

Authors:  Jo Vandesompele; Katleen De Preter; Filip Pattyn; Bruce Poppe; Nadine Van Roy; Anne De Paepe; Frank Speleman
Journal:  Genome Biol       Date:  2002-06-18       Impact factor: 13.583

10.  The influence of tumor size and environment on gene expression in commonly used human tumor lines.

Authors:  Michael A Gieseg; Michael Z Man; Nicholas A Gorski; Steven J Madore; Eric P Kaldjian; Wilbur R Leopold
Journal:  BMC Cancer       Date:  2004-07-15       Impact factor: 4.430

View more
  53 in total

1.  Differential expression of miR-139, miR-486 and miR-21 in breast cancer patients sub-classified according to lymph node status.

Authors:  Lene Rask; Eva Balslev; Rolf Søkilde; Estrid Høgdall; Henrik Flyger; Jens Eriksen; Thomas Litman
Journal:  Cell Oncol (Dordr)       Date:  2014-07-16       Impact factor: 6.730

2.  Immune gene expression in kidney biopsies of lupus nephritis patients at diagnosis and at renal flare.

Authors:  Juan M Mejia-Vilet; Samir V Parikh; Huijuan Song; Paolo Fadda; John P Shapiro; Isabelle Ayoub; Lianbo Yu; Jianying Zhang; Norma Uribe-Uribe; Brad H Rovin
Journal:  Nephrol Dial Transplant       Date:  2019-07-01       Impact factor: 5.992

Review 3.  Future microfluidic and nanofluidic modular platforms for nucleic acid liquid biopsy in precision medicine.

Authors:  Ana Egatz-Gomez; Ceming Wang; Flora Klacsmann; Zehao Pan; Steve Marczak; Yunshan Wang; Gongchen Sun; Satyajyoti Senapati; Hsueh-Chia Chang
Journal:  Biomicrofluidics       Date:  2016-05-05       Impact factor: 2.800

4.  Fluorescence activated cell sorting followed by small RNA sequencing reveals stable microRNA expression during cell cycle progression.

Authors:  Vince Kornél Grolmusz; Eszter Angéla Tóth; Kornélia Baghy; István Likó; Ottó Darvasi; Ilona Kovalszky; János Matkó; Károly Rácz; Attila Patócs
Journal:  BMC Genomics       Date:  2016-05-27       Impact factor: 3.969

5.  Salivary microRNAs identified by small RNA sequencing and machine learning as potential biomarkers of alcohol dependence.

Authors:  Andrew J Rosato; Xiaochun Chen; Yoshiaki Tanaka; Lindsay A Farrer; Henry R Kranzler; Yaira Z Nunez; David C Henderson; Joel Gelernter; Huiping Zhang
Journal:  Epigenomics       Date:  2019-05-29       Impact factor: 4.778

Review 6.  Epigenetics in Kidney Transplantation: Current Evidence, Predictions, and Future Research Directions.

Authors:  Valeria R Mas; Thu H Le; Daniel G Maluf
Journal:  Transplantation       Date:  2016-01       Impact factor: 4.939

7.  Retrospective MicroRNA Sequencing: Complementary DNA Library Preparation Protocol Using Formalin-fixed Paraffin-embedded RNA Specimens.

Authors:  Olivier Loudig; Christina Liu; Thomas Rohan; Iddo Z Ben-Dov
Journal:  J Vis Exp       Date:  2018-05-05       Impact factor: 1.355

Review 8.  Emerging Biosensing Approaches for microRNA Analysis.

Authors:  Richard M Graybill; Ryan C Bailey
Journal:  Anal Chem       Date:  2015-12-21       Impact factor: 6.986

Review 9.  Mini but mighty: microRNAs in the pathobiology of periodontal disease.

Authors:  Moritz Kebschull; Panos N Papapanou
Journal:  Periodontol 2000       Date:  2015-10       Impact factor: 7.589

Review 10.  MicroRNAs in kidney transplantation.

Authors:  Julia Wilflingseder; Roman Reindl-Schwaighofer; Judith Sunzenauer; Alexander Kainz; Andreas Heinzel; Bernd Mayer; Rainer Oberbauer
Journal:  Nephrol Dial Transplant       Date:  2014-08-28       Impact factor: 5.992

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.